Light-weight localization for vehicles using road markings

By Ananth Ranganathan, David Ilstrup, Tao Wu. 2013

58 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Ryan W. Wolcott
2. Ryan M. Eustice
3. Jae Kyu Suhr
4. D. Min
5. J. Jang
6. Ho Gi Jung
7. Khaled Ben Letaief
8. Jun Zhang

In Computer Vision and Pattern Recognition

1. Ryan W. Wolcott
2. Ryan M. Eustice
3. Jae Kyu Suhr
4. D. Min
5. J. Jang
6. Ho Gi Jung

In Media Technology

1. Ryan M. Eustice
2. Ryan W. Wolcott

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, neutral): “computational requirements and environmental challenges, running SLAM algorithms outdoors, which is the operational domain of ADSs, is less efficient than localization with a pre-built map [112].”
• Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments (2017, background): “Ranganathan et al. [25] and Wu and Ranganathan [26] localize the ego-vehicle by recognizing ten types of arrows and characters.”
• Visual localization within LIDAR maps for automated urban driving (2014, background): “In the context of autonomous vehicles, Wu and Ranganathan [4], [9] try to circumvent this by identifying and extracting higher fidelity features from road markings in images that are far more robust and representative of static infrastructure.”